Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add meltedinhex/analyst-ai-pack --skill hunting-from-a-threat-intel-reportgit clone --depth 1 https://github.com/meltedinhex/analyst-ai-packWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/hunting-from-a-threat-intel-report)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/hunting-from-a-threat-intel-report"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/hunting-from-a-threat-intel-report/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/hunting-from-a-threat-intel-report"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/hunting-from-a-threat-intel-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00077 | $0.00783 |
| Opus 5 | $0.00039 | $0.00392 |
| Sonnet 5 | $0.00015 | $0.00157 |
| Haiku 4.5 | $0.00008 | $0.00078 |
Grade A, and why
hunting-from-a-threat-intel-report scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hunting from a Threat Intel Report
When to Use
- You received a CTI report (vendor writeup, ISAC bulletin, IR report) and must operationalize it.
- You want to convert narrative TTPs and IOC lists into concrete hunts against your telemetry.
- You need to prioritize which indicators are worth hunting given they age at different rates.
Do not use an IOC blocklist as the whole engagement — atomic indicators (hashes, IPs) are trivially changed; durable value comes from hunting the behaviors (TTPs).
Prerequisites
- The report and a way to extract its IOCs and behavioral claims.
- Knowledge of your telemetry coverage to judge which TTPs are huntable.
Workflow
Step 1: Extract IOCs and TTPs
Pull atomic indicators (hashes, IPs, domains, URLs) and the behavioral TTPs (the report's "how"). Defang/normalize indicators for safe handling.
python scripts/analyst.py extract report.txt
Step 2: Map to ATT&CK and the Pyramid of Pain
Tag behaviors with techniques and rank indicators by the Pyramid of Pain — prioritize TTPs and tools over hashes/IPs because they cost the adversary more to change.
Step 3: Check telemetry feasibility
For each TTP, confirm you have the data source to hunt it; note gaps as detection-engineering work.
Step 4: Build concrete hunts
Translate the high-value TTPs into queries (Sysmon, DNS, proxy, EDR), and sweep atomic IOCs as a quick first pass for current presence.
Step 5: Execute, document, and feed back
Run the hunts, record findings/gaps/negatives, escalate hits to IR, and convert durable logic into detections (Sigma).
Validation
- Both atomic IOCs and behavioral TTPs are extracted, not just the indicator list.
- Hunts target the highest-pain indicators feasible with your telemetry.
- Each TTP maps to a real data source or is logged as a coverage gap.
Pitfalls
- Stopping at IOC sweeps; the adversary rotates them and you miss the campaign.
- Hunting TTPs you have no telemetry for, producing false confidence.
- Failing to defang indicators, risking accidental execution/clicks.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 94 lines · 77 tokens per session scan A a96cbe006ad5
hunting-from-a-threat-intel-report is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 77 tokens to every session and 783 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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